The analysis of a Holter recording, i.e., recorded ECG signals, requires a rather complex examination. Indeed, the uninterrupted recording of an ECG signal of a patient over a 24 or 48 hour period represents approximately 100,000 PQRST complexes. It is thus necessary to analyze the variability of these complexes so as to search for any pathological event, such as a rhythm disorder, cardiac anoxia, operating anomaly of a cardiac pacemaker, etc. This analysis, which is typically carried out automatically by algorithms implemented in software executed in a computer (e.g., a microprocessor with associated memory, data registers, etc.) remote to an implanted device or included in an ambulatory apparatus, provides intermediate results, namely a synthesis of the data from which the doctor will be able to make a diagnosis.
These algorithms process a large volume of data. Therefore, a relatively large data processing means is required. As a result, the optimization of the algorithms, in terms of effectiveness compared to the required processor resources (e.g., memory size, speed, bit resolution, battery requirements as appropriate, etc.), is a significant factor in the field of the analysis of the physiological signals, and in particular with the Holter recording of signals.
Another difficulty with the known Holter recorder systems lies in the error rate in the analysis of the signals, which can have serious consequences, with in particular a risk of false diagnosis. Indeed, in the particular case of an ambulatory recorded ECG, the signal is not regular and numerous artifacts are present. More specifically, the ECG signal is generally made up of a signal that is cardiac in origin, almost periodic (namely, the so-called “PQRST” complex), accompanied by parasitic signals such as those generated by the muscles, by the mechanical disturbances on the electrode-skin interface, and by the electric or electromagnetic interference collected by the cables connecting the electrodes to the recorder.
The traditional algorithms are able to detect and filter out the parasitic signals, but not totally, and thus can lead to an error rate in the identification of the cardiac signal. Even if the traditional algorithms are able to reach typically an error rate of 0.1%, this represents approximately 100 errors during a 24 hour recording (approximately 100,000 PQRST complexes). It constitutes an error level still considered too high. This is because these errors can be concomitant with complexes presenting singularities that are significant from the point of view of making an appropriate diagnosis.
In addition, for an ECG signal, it is significant to be able to observe the variability of the QRS complex, which can be very meaningful for making a diagnosis. The analysis algorithm used must thus be able to reveal and discriminate a certain number of micro-variations.
The automatic analysis of an ECG signal generally comprises three distinct stages, which are: 1) the preliminary conditioning and filtering of the signal, so as to eliminate a certain number of parasites in the frequency field and to deliver a better quality signal; 2) the decomposition and identification of the characteristic waves of the signal; and 3) a synthesis of the temporal evolution of the parameters describing these various characteristic waves. These results make it possible for the doctor to establish a diagnosis, and it is obvious that the analysis results must be at the same time reliable and relevant to facilitate this diagnosis.
The reliability rests partly on the robustness and the good adaptation of the decomposition and the identification realized at the second stage. More particularly, the ECG signal is presented in the form illustrated on FIG. 1, which is a tracing representing the evolution over time of the electrical activity of the heart, with a succession of waves, having a positive or negative amplitude, on both sides of a line characteristic of the cardiac phase of rest known as “isoelectric line.” During a normal cardiac beat (illustrated on FIG. 1), these positive or negative waves are identified as resulting from well defined physiological processes, making it possible to allot to each wave a standardized label, typically P, Q, R, S or T. Physiologically, the P wave is generated by the depolarization of the atrium, the QRS waves by the depolarization of the ventricle, and the T wave by the re-polarization of the ventricle. Based on the form and of the temporal position of these various waves, as well as their variability, the doctor will be able to recognize a given pathology.
Several processes of decomposition and identification of the characteristic waves of the signal have been proposed. One, a frequency analysis, makes it possible to describe the signal in the Fourier space (i.e., a transformation of the data acquired in the time domain to data in a frequency domain). However, such a decomposition is not completely adapted to the analysis of an ECG signal because this signal is not rigorously periodic. It has rich spectral contents that vary in time. Moreover, the sinusoidal functions of the frequency decomposition do not make it possible to obtain the phase of the signal, which is necessary to an identification of the component waves; indeed misadaptations that occur with such a frequency decomposition may lose the temporal phase information.
To carry out a time-frequency decomposition, one proposed technique is to use the transform in non-orthogonal wavelets, the identification of the waves then being done on the time-frequency content of the wavelets that model the signal. However, this method has a lack of resolution that quickly becomes a limit for a fine analysis. This difficulty can be mitigated by a decomposition in non-orthogonal wavelets (or in basic radial functions, or Gaussian, etc.). In this regard, the ECG signal is decomposed into a Gaussian sum which are of either a fixed or adaptable size. This method has been used but suffers from a characteristic handicap due to the fact that the waves to be modeled (i.e., the P, Q, R, S and T waves) are not really Gaussian, so that their modeling requires the use of a very great number of parameters to be of sufficient quality. This in turn requires considerable computing power to be able to be implemented in a reasonable time. Moreover, if the result is correct for QRS wave, then the P wave and the T wave, which are not easily comparable to a Gaussian distribution, are rather badly modeled: one must then use a great number of parameters to obtain a sufficient quality, thus leading to an excessive complexity of the analysis algorithm.
In view of the various issues, it also is known to use a simple linear decomposition, where the fluctuations of amplitude are replaced by straight line segments as soon as the derivative of the signal becomes significant. The result is a modeled signal made up of a succession of straight line segments that are then very simple to treat: For example, a monophasic wave is made of a succession of two segments of opposite directions, and a biphasic wave is made of three segments of opposite directions. This last technique is very effective, but reaches its limits when it applies to particular cases such as, for example, pertubating low frequency waves that lead to an over-decomposition in multiple segments that are then difficult to analyze.